STI Publications - View Publication Form #17608
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Publication Information
Title | Charged Particle Reconstruction in CLAS12 using Machine Learning | ||||
Abstract | In this work, we present studies of track parameter reconstruction from raw information in CLAS12 detector's Drift Chambers, using Machine Learning (ML). We study the resolution of tracks reconstructed with different types of ML models/algorithms, including Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Gradient Boosting Trees (GBT) using simulated data. The resulting ML model is capable of reconstructing track parameters (particle momentum, and polar and azimuthal angles) with accuracy similar to Hit Based (HB) tracking code, but $150$ times faster. Moreover, physics reactions can be identified using the particles reconstructed by the neural network in real-time (with a rate of about $34~kHz$) during experimental data collection. The developed model can be used in numerous applications, such as triggering specific physics reactions in real-time, detector performance monitoring, and real-time detector calibration. | ||||
Author(s) | Gagik Gavalian, Polykarpos Thomadakis, Kevin Garner, Nikos Chrisochoides | ||||
Publication Date | June 2023 | ||||
Document Type | Journal Article | ||||
Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
Affiliation | Exp Nuclear Physics / Experimental Halls / Hall B | ||||
Funding Source | Nuclear Physics (NP) | ||||
Proprietary? | No | ||||
This publication conveys | Technical Science Results | ||||
Document Numbers |
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Associated with an experiment | Yes | ||||
Experiment Number(s) |
E12-06-112
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Associated with EIC | No | ||||
Supported by Jefferson Lab LDRD Funding | No |
Journal Article
Journal Name | Computer Physics Communication |
Refereed | Yes |
Volume | 287 |
Issue | 1 |
Page(s) | 108694 |
Attachments/Datasets/DOI Link
Document(s) |
main45.pdf
(STI Document)
main45.pdf
(Accepted Manuscript)
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DOI Link | |
Dataset(s) | (none) |
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